{"id":"W1867104150","doi":"10.1109/ccece.2000.849556","title":"A summary of applications of Hopfield neural network to economic load dispatch","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Economic dispatch; Computer science; Artificial neural network; Mathematical optimization; Hopfield network; Electric power system; Scheduling (production processes); Operating cost; Power Balance; Process (computing); Optimization problem; Power (physics); Engineering; Artificial intelligence; Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005953772,0.00007235028,0.0001348247,0.00002617873,0.00004521967,0.00001803964,0.0006242162,0.00002846452,0.00009653964],"category_scores_gemma":[0.000001772255,0.00006522259,0.0000592916,0.000312292,0.00002392772,0.0001051933,0.0001836847,0.00005189616,0.00005647089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001370033,"about_ca_system_score_gemma":0.00001172999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001022333,"about_ca_topic_score_gemma":0.00008974611,"domain_scores_codex":[0.9992835,0.00001107448,0.0002460929,0.0002140073,0.00008350046,0.0001618543],"domain_scores_gemma":[0.9991586,0.00008701907,0.00007918507,0.0005626921,0.00003541008,0.00007708085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005330352,0.0002083218,0.004318546,0.00002507764,0.00002595431,6.230918e-7,0.000207421,0.07209567,0.0008255247,0.3033871,0.331762,0.2871384],"study_design_scores_gemma":[0.0002916167,0.0001617819,0.00347886,0.00002629376,0.00001384242,0.000006244241,0.00001712569,0.8755525,0.001878917,0.008001479,0.110211,0.0003603673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02438787,0.0003753238,0.9380132,0.009519202,0.0001656612,0.000763961,0.00001015262,0.0001269014,0.02663775],"genre_scores_gemma":[0.9736648,0.00005134116,0.02396342,0.0007229766,0.0001275391,0.0001052273,0.000001067546,0.000005301637,0.001358294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.949277,"threshold_uncertainty_score":0.26597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462651454418475,"score_gpt":0.2300528988280921,"score_spread":0.2154263842839074,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}